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Measuring Similarity Between Contexts and Concepts

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University of Minnesota, Duluth
University of Minnesota, Duluth

The document discusses methods for measuring similarity between concepts and contexts. It describes approaches that measure conceptual similarity using structured knowledge bases like WordNet and contextual similarity using co-occurrence information from large corpora. Word sense disambiguation can be performed by finding the sense of a word most related to its neighbors based on these similarity measures. The document also discusses limitations and opportunities for improving current approaches.

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Measuring Similarity Between Concepts and Contexts Ted Pedersen Department of Computer Science University of Minnesota, Duluth http://www.d.umn.edu/~tpederse
The problems… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Similarity and Relatedness ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The approaches… ,[object Object],[object Object],[object Object]
Why measure conceptual similarity? ,[object Object],[object Object],[object Object],[object Object]
Word Sense Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SenseRelate ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WordNet::Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
watercraft instrumentality object artifact conveyance vehicle motor-vehicle car boat ark article ware table-ware cutlery fork from Jiang and Conrath [1997]
Path Finding ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
watercraft instrumentality object artifact conveyance vehicle motor-vehicle car boat ark article ware table-ware cutlery fork
Information Content ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Observed “car”... motor vehicle (327 +1) *root* (32783 + 1) minicab (6) cab (23) car (73 +1) bus (17) stock car (12)
Observed “stock car”... motor vehicle (328+1) *root* (32784+1) minicab (6) cab (23) car (74+1) bus (17) stock car (12+1)
After Counting Concepts... motor vehicle (329) IC = 1.998 *root* (32785) minicab (6) cab (23) car (75) bus (17) stock car (13) IC = 3.042
Similarity and Information Content ,[object Object],[object Object],[object Object]
Why doesn’t this solve problem? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Using Dictionary Glosses to Measure Relatedness ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Context/Gloss Vectors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Gloss/Context Vectors
Experiment ,[object Object],[object Object],[object Object],[object Object]
Results ,[object Object],[object Object],[object Object],[object Object]
Why this doesn’t solve the problem.. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Knowledge Lean Methods ,[object Object],[object Object]
Word Sense Discrimination ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Name Discrimination ,[object Object],[object Object],[object Object],[object Object]
 
 
 
 
Objective ,[object Object],[object Object],[object Object]
Similarity of Context? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Feature Selection ,[object Object],[object Object],[object Object],[object Object],[object Object]
Second Order Context Representation ,[object Object],[object Object],[object Object],[object Object],[object Object]
2 nd Order Context Vectors ,[object Object],0 6272.85 2.9133 62.6084 20.032 1176.84 51.021 O2 context 0 18818.55 0 0 0 205.5469 134.5102 guy 0 0 0 136.0441 29.576 0 0 Oscar 0 0 8.7399 51.7812 30.520 3324.98 18.5533 won needle family war movie actor football baseball
Limitations of 2 nd order 0 52.27 0 0.92 0 4.21 0 28.72 0 3.24 0 1.28 0 2.53 Weapon Missile Shoot Fire Destroy Murder Kill 17.77 0 14.6 46.2 22.1 0 34.2 19.23 2.36 0 72.7 0 1.28 2.56 Execute Command Bomb Pipe Fire CD Burn
Singular Value Decomposition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
After context representation… ,[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation (before mapping) c1 c2 c4 c3 2 1 15 2 C4 6 1 1 2 C3 1 7 1 1 C2 2 3 0 10 C1
Evaluation (after mapping) Agreement=38/55=0.69 20 15 2 1 2 C4 17 1 1 0 55 11 12 15 10 6 1 2 C3 10 1 7 1 C2 15 2 3 10 C1
Majority Sense Classifier Maj. =17/55=0.31
Experimental Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Name Conflated Data 51.4% 231,069 JapAnce 112,357 France 118,712 Japan 53.9% 46,431 JorGypt 21,762 Egyptian 25,539 Jordan 56.0% 13,734 MonSlo 6,176 Slobodan Milosovic 7,846 Shimon Peres 58.6% 5,807 MSIIBM 2,406 IBM 3,401 Microsoft 73.7% 4,073 JikRol 1,071 Rolf Ekeus 3,002 Tajik 69.3% 2,452 RoBeck 740 David Beckham 1,652 Ronaldo Maj. Total New Count Name Count Name
50.3 50.3 51.1 51.1 51.4 231,069 JapAnce 53.0 57.0 59.1 56.6 53.9 46,431 JorGypt 91.4 54.6 96.6 62.8 56.0 13,734 MonSLo 60.0 68.0 51.3 47.7 58.6 5,807 MSIIBM 90.4 91.0 96.2 94.7 73.7 4,073 JikRol 54.7 85.9 72.7 57.3 69.3 2,452 Robeck Ft 20 Ft 5 Ft 20 Ft 5 Maj. # Cxt 20 Cxt 5
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Ongoing work ,[object Object],[object Object],[object Object],[object Object]
Thanks to… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Measuring Similarity Between Contexts and Concepts

  • 1. Measuring Similarity Between Concepts and Contexts Ted Pedersen Department of Computer Science University of Minnesota, Duluth http://www.d.umn.edu/~tpederse
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  • 9. watercraft instrumentality object artifact conveyance vehicle motor-vehicle car boat ark article ware table-ware cutlery fork from Jiang and Conrath [1997]
  • 10.
  • 11. watercraft instrumentality object artifact conveyance vehicle motor-vehicle car boat ark article ware table-ware cutlery fork
  • 12.
  • 13. Observed “car”... motor vehicle (327 +1) *root* (32783 + 1) minicab (6) cab (23) car (73 +1) bus (17) stock car (12)
  • 14. Observed “stock car”... motor vehicle (328+1) *root* (32784+1) minicab (6) cab (23) car (74+1) bus (17) stock car (12+1)
  • 15. After Counting Concepts... motor vehicle (329) IC = 1.998 *root* (32785) minicab (6) cab (23) car (75) bus (17) stock car (13) IC = 3.042
  • 16.
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  • 35.
  • 36. Limitations of 2 nd order 0 52.27 0 0.92 0 4.21 0 28.72 0 3.24 0 1.28 0 2.53 Weapon Missile Shoot Fire Destroy Murder Kill 17.77 0 14.6 46.2 22.1 0 34.2 19.23 2.36 0 72.7 0 1.28 2.56 Execute Command Bomb Pipe Fire CD Burn
  • 37.
  • 38.
  • 39. Evaluation (before mapping) c1 c2 c4 c3 2 1 15 2 C4 6 1 1 2 C3 1 7 1 1 C2 2 3 0 10 C1
  • 40. Evaluation (after mapping) Agreement=38/55=0.69 20 15 2 1 2 C4 17 1 1 0 55 11 12 15 10 6 1 2 C3 10 1 7 1 C2 15 2 3 10 C1
  • 41. Majority Sense Classifier Maj. =17/55=0.31
  • 42.
  • 43. Name Conflated Data 51.4% 231,069 JapAnce 112,357 France 118,712 Japan 53.9% 46,431 JorGypt 21,762 Egyptian 25,539 Jordan 56.0% 13,734 MonSlo 6,176 Slobodan Milosovic 7,846 Shimon Peres 58.6% 5,807 MSIIBM 2,406 IBM 3,401 Microsoft 73.7% 4,073 JikRol 1,071 Rolf Ekeus 3,002 Tajik 69.3% 2,452 RoBeck 740 David Beckham 1,652 Ronaldo Maj. Total New Count Name Count Name
  • 44. 50.3 50.3 51.1 51.1 51.4 231,069 JapAnce 53.0 57.0 59.1 56.6 53.9 46,431 JorGypt 91.4 54.6 96.6 62.8 56.0 13,734 MonSLo 60.0 68.0 51.3 47.7 58.6 5,807 MSIIBM 90.4 91.0 96.2 94.7 73.7 4,073 JikRol 54.7 85.9 72.7 57.3 69.3 2,452 Robeck Ft 20 Ft 5 Ft 20 Ft 5 Maj. # Cxt 20 Cxt 5
  • 45.
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  • 47.